Data Stream Classification by Dynamic Incremental Semi-Supervised Fuzzy Clustering

doi:10.1142/s0218213019600091

Data Stream Classification by Dynamic Incremental Semi-Supervised Fuzzy Clustering

2019-12-02

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DOI：10.1142/s0218213019600091

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A data stream classification method called DISSFCM (Dynamic Incremental Semi-Supervised FCM) is presented, which is based on an incremental semi-supervised fuzzy clustering algorithm. The method assumes that partially labeled data belonging to different classes are continuously available during time in form of chunks. Each chunk is processed by semi-supervised fuzzy clustering leading to a cluster-based classification model. The proposed DISSFCM is capable of dynamically adapting the number of clusters to data streams, by splitting low-quality clusters so as to improve classification quality. Experimental results on both synthetic and real-world data show the effectiveness of the proposed method in data stream classification.

Data Stream Classification by Dynamic Incremental Semi-Supervised Fuzzy Clustering

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